Jaime Hernandez-Cordero

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This paper describes improvements to an innovative high-performance speaker recognition system. Recent experiments showed that with sufficient training data phone strings from multiple languages are exceptional features for speaker recognition. The prototype phonetic speaker recognition system used phone sequences from six languages to produce an equal(More)
In 2012 NIST held the latest in an ongoing series of textindependent speaker recognition evaluations (SRE’s). The 2012 NIST Speaker Recognition Evaluation (SRE12) was the largest and most complex SRE to date, including over 100 million trials. Several aspects of SRE12 were new; most significantly, NIST released in advance of the evaluation target speaker(More)
We discuss two NIST coordinated evaluations of automatic language recognition technology planned for calendar year 2015 along with possible additional plans for the future. The first is the Language Recognition i-Vector Machine Learning Challenge, largely modeled on the 2013-2014 Speaker Recognition i-Vector Machine Learning Challenge. This online(More)
This paper describes a newly realized highperformance speaker recognition system and examines methods for its improvement. Innovative experiments early this year showed that phone strings are exceptional features for speaker recognition. The original system produced equal error rates less than 11.5% on Switchboard-I audio files. Subsequent research(More)
In 2015 NIST coordinated the first language recognition evaluation (LRE) that used i-vectors as input, with the goals of attracting researchers outside of the speech processing community to tackle the language recognition problem, exploring new ideas in machine learning for use in language recognition, and improving recognition accuracy. The Language(More)
Short-time spectral characterizations of the human voice have proven to be the most dependable features available to modern speaker recognition systems. However, it is well-known that highlevel linguistic information such as word usage and pronunciation patterns can provide complementary discriminative power. In an automatic setting, the availability of(More)
In late 2013 and 2014, the National Institute of Standards and Technology (NIST) coordinated an i-vector challenge utilizing data from previous NIST Speaker Recognition Evaluations. Following the evaluation period, a second phase of the challenge was held, where speaker labels were made available for system development. The second phase included system(More)
In 2015, NIST conducted the most recent in an ongoing series of Language Recognition Evaluations (LRE) meant to foster research in language recognition. The 2015 Language Recognition Evaluation featured 20 target languages grouped into 6 language clusters. The evaluation was focused on distinguishing languages within each cluster, without disclosing which(More)
  • Alex, Synthèse L, +43 authors D D Palmer Vii Foreword
  • 2001
The NATO Native and Non-Native (N4) corpus has been developed by the NATO research group on Speech and Language Technology, in order to provide a militaryoriented database for multilingual and non-native speech processing studies. Speech data has been recorded in the Naval transmission training centers of four countries (Germany, The Netherlands, UK and(More)
In 2016, the National Institute of Standards and Technology (NIST) conducted the most recent in an ongoing series of speaker recognition evaluations (SRE) to foster research in robust text-independent speaker recognition, as well as measure performance of current state-of-the-art systems. Compared to previous NIST SREs, SRE16 introduced several new aspects(More)
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